AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones, so Active Attention Probing is introduced, which supplies a fixed content independent measurement coordinate.
It is demonstrated that residual-authority replay arises in practice and substantially increases the success of prompt-injection and context-rebinding attacks, exposing a fundamental mismatch between persistent authorization and the contextual nature of user consent in long-lived LLM agents.
Experiments on five real-world pixel-level image processing tasks show that PixCrypt significantly improves the practicality of FHE for privacy-preserving analytics, yielding up to 35x faster fine-grained encryption and maintains IND-CPA (Indistinguishability under Chosen Plaintext Attack) security.
ActProbe, an internal-state-based framework for detecting and localizing poisoned segments in multi-source LLM inputs, is proposed and remains effective against defense-aware adaptive attacks and can protect black-box APIs through surrogate-based poisoned-segment removal.
ToSS (Token Oriented Repartitioning and Strategic Selection), a reliable authentication method using adaptive dual watermarking that divides vocabulary tokens into black and white sublists, enabling precise bit-level embedding of traceability information.
AcMAS is proposed, an activation-based framework for malicious-behavior detection in MAS that significantly outperforms graph-based baselines against stealthy attacks, with generalization across diverse open-source LLM backbones, attack intensity, and MAS scale.
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